In a typical communication system, forward error correction (FEC) is often applied in order to improve robustness of the system against a wide range of impairments of the communication channel.
Referring to FIG. 1, in which a typical communication network channel is depicted having an information source 101, sending data to a source coder 102 that in turn forwards the data to a channel encoder 103. The encoded data is then modulated 104 onto a carrier before being transmitted over a channel 105. After transmission, a like series of operations takes place at the receiver using a demodulator 106, channel decoder 107 and source decoder 108 to produce data suitable for the information sink 109. FEC is applied by encoding the information data stream at the transmit side at the encoder 103, and performing the inverse decoding operation on the receive side at the decoder 107. Encoding usually involves generation of redundant (parity) bits that allow more reliable reconstruction of the information bits at the receiver.
In many modern communication systems, FEC uses Low-Density Parity-Check (LDPC) codes that are applied to a block of information data of the finite length.
One way to represent LDPC codes is by using so-called Tanner graphs, in which N symbol nodes, correspond to bits of the codeword, and M check nodes, correspond to the set of parity-check constraints which define the code. Edges in the graph connect symbol nodes to check nodes.
LDPC codes can also be specified by a parity check matrix H of size M×N. In the matrix H, each column corresponds to one of the symbol nodes while each row corresponds to one of the check nodes. This matrix defines an LDPC block code (N, K), where K is the information block size, N is the length of the codeword, and M is the number of parity check bits. M=N−K. A general characteristic of the LDPC parity check matrix is the low density of non-zero elements that allows utilization of efficient decoding algorithms. The structure of the LDPC code parity check matrix is first outlined in the context of existing hardware architectures that can exploit the properties of these parity check matrices.
In order to accommodate various larger code rates without redesigning parity check matrix and therefore avoiding changing significantly base hardware wiring, expansion of a base parity check matrix is one of the common approach. This may be archived, for example, by replacing each non-zero element by a permutation matrix of the size of the expansion factor.
One problem often faced by the designer of LDPC codes is that the base parity check matrices are designed to follow some assumed degree distribution, which is defined as the distribution of column weights of the parity check matrix. Column weight in turn equals the number of in a column. It has been shown that irregular degree distributions offer the best performance on the additive white Gaussian noise channel. However, the base parity check matrix does not exhibit any structure in its Hd portion to indicate the final matrix after expansion. The number of sub-matrix blocks, corresponding to the number of sub-iterations in the layered decoding algorithm may become large. Since the maximum number of rows that can be processed in parallel equals the number of rows in the sub-matrix block, the overall throughput may be impacted.
Another problem is that in order to maintain the performance such as coding gain as high as possible, there are different requirements such as to select the largest suitable codeword from the available set of codewords and then properly adjust the amount of shortening and puncturing; use as few of the modulated symbols as possible; and keep the overall complexity at a reasonable level.
Some attempts have been made to enhance the throughput by reducing the number of rows of the base parity matrix, and consequently the number of block of rows in the expanded parity check matrix, by combining rows as a method to increase the code rate without changing the degree distribution. However, the derived high rate matrix is still relatively large, since in order to allow row combining, the original low rate base parity matrix usually has a large number of rows. The decoding time also becomes a function of the code rate: the higher the code rate the less layers in the layered decoding and, in general, less decoding time.
Other existing approaches for shortening and puncturing of the expanded matrices may preserve the column weight distribution, but may severely disturb the row weight distribution of the original matrix. This, in turn, causes degradation when common iterative decoding algorithms are used. This adverse effect strongly depends on the structure of the expanded matrix.
Therefore, there is an unmet need for a method, a system to design structured base parity check matrices, in combination with expansion, allow achieving high throughput, low latency, and at the same time, the preservation of the simple encoding feature of the expanded codeword.
There is further an unmet need for a method and a system to enable flexible rate adjustments by using shortening, or puncturing, or a combination of shortening and puncturing; and at the same time the code rate is approximately the same as the original one, and the coding gain is preserved.